Image segmentation view improvement #1131
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| @@ -6,6 +6,8 @@ | |||
| import matplotlib as mpl | |||
| import numpy as np | |||
| import PIL.Image | |||
| import json | |||
| import skfmm | |||
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@lukeyeager usually prefers seeing non-standard package imports below (line 14)
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@gheinrich, this seems PEP8 compliant (https://www.python.org/dev/peps/pep-0008/#imports) in the third party block.
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Just move json up above import os
| if self.class_labels: | ||
| text = "%s (%s)" % (text, self.class_labels[int(c)]) | ||
| legend.append({'image': image, 'text': text}) | ||
| legend = self.get_legend_for(self.found_classes) |
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I think we're not using the legend anymore in the header template, correct?
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Correct. Great catch.
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| # remember the classes we found | ||
| found_classes = np.unique(data) | ||
| found_classes = np.unique(class_data) |
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do we still need self.found_classes below? Now that we have one legend per image I think this can go away.
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Damn, you're good.
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| zeros = np.zeros(class_data.shape) | ||
| # Assuming that class 0 is the background | ||
| mask = np.greater(class_data, zeros) |
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do you have to create the zeros array? I think you can use np.greater to compare against a scalar:
>>> x=np.array([0,1,2,0,4])
>>> mask=np.greater(x,0)
>>> mask
array([False, True, True, False, True], dtype=bool)
| fill_data[:, :, 3] = mask * 255 | ||
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| # Black mask of non-segmented pixels | ||
| mask_data = np.zeros(fill_data.shape, dtype='uint8') |
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do you need this line? I think the below one is enough (?)
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The np.zeros create RGBA data. the line below sets the A component. Is there some pythonic trickery that I'm missing?
| # Assuming that class 0 is the background. | ||
| line_mask = np.zeros(class_data.shape, dtype=bool) | ||
| for c in (x for x in found_classes if x != 0): | ||
| c_mask = np.equal(class_data, zeros + c) |
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I think you can also do without zeros here:
>>> x=np.array([0,1,1,2])
>>> np.equal(x,1)
array([False, True, True, False], dtype=bool)
| # Find the signed distance from the zero contour | ||
| distance = skfmm.distance(c_mask.astype('float32') - 0.5) | ||
| # Accumulate the mask for all classes | ||
| line_mask |= c_mask & np.less(distance, zeros + 3) |
| @@ -38,7 +38,7 @@ def __init__(self, model, images, epoch, layers, resize=True, **kwargs): | |||
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| @override | |||
| def __getstate__(self): | |||
| fields_to_save = ['_id', '_name'] | |||
| fields_to_save = ['_id', '_name', 'status_history'] | |||
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can you explain this change?
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Not for this commit. I needed for something a while back, which I no longer recall. Removing.
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| # add the outlines to the input image | ||
| for x in xrange(3): | ||
| input_data[:, :, x] = (input_data[:, :, x] * (1 - line_mask) + |
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this doesn't work if input_data is a grayscale image (with shape e.g. [256,256])
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also, this should be done within the if len(found_classes)>1 otherwise line_mask is uninitialized
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@gheinrich, did you try this with a (256, 256)?
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@jmancewicz yes, this was an image from the Sunnybrook dataset (with channel conversion = 'none')
| fill_data[:, :, x] * line_mask) | ||
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| # Input image with outlines | ||
| input_image = PIL.Image.fromarray(input_data) |
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does this work if input_data.dtype!='uint8'?
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No, but that's in an upcoming PR.
…w-improvement Image segmentation view improvement
Changed the imageSegmentation view extension so that rather than displaying the segmentation image,
The segmentation image overlays the input image with a solid outline around each class.
The opacity of the segmented regions and the opacity of the mask of the non-segmented regions are adjustable.
I've removed the color to class legend from the header and put a legend or relevant classes beneath each image. When the pointer is over a segmented region, the corresponding class in the legend is highlighted.